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Addressing Complexity in System of Systems With GraphRAG: An AI‐Driven Framework for Dynamic Data Integration.

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Bibliographic Details
Title: Addressing Complexity in System of Systems With GraphRAG: An AI‐Driven Framework for Dynamic Data Integration.
Authors: Huang, Yinchien1 (AUTHOR) huan1903@purdue.edu, Fung, Tien‐Yueh1 (AUTHOR), DeLaurentis, Daniel A.2 (AUTHOR)
Source: Systems Engineering. Oct2025, p1. 19p. 13 Illustrations.
Subject Terms: *SYSTEM of systems, *DATA integration, *MODEL-driven software architecture, *ARTIFICIAL intelligence, *KNOWLEDGE graphs, *ENTROPY (Information theory)
Abstract: ABSTRACT System of Systems (SoS) environments are inherently complex, involving numerous operationally and managerially independent component systems with hidden interdependencies and frequent interactions based on unstructured data. In this paper, we propose using graphical Retrieval‐Augmented Generation (GraphRAG), a tool that combines large language models with knowledge graph (KG) techniques to address these challenges. Using metrics from information entropy and graph theory, we demonstrate how KG construction and clustering nodes can reduce complexity in SoS. An example application in the Urban Air Mobility setting illustrates that GraphRAG can solve concrete data integration challenges while outperforming traditional Retrieval‐Augmented Generation (RAG) methods. The upshot is improved execution of model‐based systems engineering in an SoS context: mitigating risks from incomplete information, enhancing system integration, and improving decision‐making. [ABSTRACT FROM AUTHOR]
Database: Academic Search Index
Description
Abstract:ABSTRACT System of Systems (SoS) environments are inherently complex, involving numerous operationally and managerially independent component systems with hidden interdependencies and frequent interactions based on unstructured data. In this paper, we propose using graphical Retrieval‐Augmented Generation (GraphRAG), a tool that combines large language models with knowledge graph (KG) techniques to address these challenges. Using metrics from information entropy and graph theory, we demonstrate how KG construction and clustering nodes can reduce complexity in SoS. An example application in the Urban Air Mobility setting illustrates that GraphRAG can solve concrete data integration challenges while outperforming traditional Retrieval‐Augmented Generation (RAG) methods. The upshot is improved execution of model‐based systems engineering in an SoS context: mitigating risks from incomplete information, enhancing system integration, and improving decision‐making. [ABSTRACT FROM AUTHOR]
ISSN:10981241
DOI:10.1002/sys.70012